ZeroGPU AI @ZeroGPU_AI
The compute efficient layer for AI inference zerogpu.ai Austin, TX Joined October 2025-
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Came across @ZeroGPU_AI (zerogpu.ai) - edge inference network that’s 10x faster, 50%+ cheaper, and 20% more accurate. Fewer tokens, better answers. This feels like the future.
We just launched on @ProductHunt 🎉 Most teams are overpaying on AI. Tasks are being sent to expensive frontier models. It’s token overkill. That’s why we’re building small language models, lowering costs by up to 50% and reducing latency by 10x.
We hit #2 on Product Hunt today. 🎉 Thanks to everyone who upvoted, commented, and shared. For a small early-stage team, this kind of support means everything. The day isn’t over yet! We’d love your support as we push for #1 👇 🔗 producthunt.com/products/zerog…
@MicroLaunchHQ @ProductHunt @its_maddy_a @thisiskp_ already #2! Let's go to #1 🚀 Thanks for your support!
We just launched on @ProductHunt 🎉 Most teams are overpaying on AI. Tasks are being sent to expensive frontier models. It’s token overkill. That’s why we’re building small language models, lowering costs by up to 50% and reducing latency by 10x.
@ArtyScienza @ProductHunt @its_maddy_a @thisiskp_ Thank you so much for your support!
NEWS: 🚨 Just hunted @ZeroGPU_AI on @ProductHunt Most AI apps send EVERYTHING through frontier models: → Classification → Moderation → PII detection → Summarization That’s like hiring a rocket scientist to sort your mail. Every. Single. Day. And then paying them every. single. time. @brian_armstrong predicts 80% of AI workloads will run on 99% cheaper models within 12-18 months. @Benioff had a similar prediction on the @theallinpod a month ago 👀 ZeroGPU is building that infrastructure. Today. Founded by my friend @its_maddy_a who’s perfect for this. ZeroGPU offers: ✅ 10x latency reduction ✅ Massive cost savings ✅ Drop-in OpenAI-compatible API ✅ Zero GPU provisioning Would love for your support for the launch 👇 producthunt.com/products/zerog…
Salesforce’s @Benioff and Coinbases’s @brian_armstrong have both echoed the need for a ZeroGPU solution in recent weeks. Because not every task needs a frontier model. Save those models for your hardest reasoning problems. Use ZeroGPU.ai for everything else.
We built ZeroGPU to bring cost-effective compute to AI development - without needing expensive new data centers in space, underwater, in your town or wherever else. Now we need your support 🙏 every vote and comment counts! producthunt.com/products/zerog…
I see this more and more often - @ZeroGPU_AI pitched by @brian_armstrong . Started with @Benioff two weeks ago at the @theallinpod We are onto something here! zerogpu.ai
Good take My guess is - demand for intelligence is near infinite - but 80% of workloads will be running on 99% cheaper models within 12-18 months - 20% of workloads will still run on latest gen models where IQ maxing is important (scientific breakthroughs, higher level
And we’re about to drop something big very soon. 👀 If you want to scale your AI app without skyrocketing your compute costs, follow now.
Enterprises are scrambling to take control of their AI costs. Throwing expensive frontier LLMs at routine tasks is the bottleneck. That’s why we’re building the compute efficiency layer for AI, letting devs offload high-volume workloads to specialized SLMs. 🧵
Check out our site to see how we're reshaping AI infrastructure: 👉 zerogpu.ai
A few weeks ago I posted about @Benioff’s comment that Salesforce expects to spend roughly $300M on @AnthropicAI tokens this year. The response was bigger than I expected 😄
@TechCrunch Enterprises are going to learn that not every task needs a frontier model. Most AI work can be done more efficiently with specialized small language and nano models. No massive data centers, just the compute that's already in our pockets. ZeroGPU.ai
TokenMaxxxing is out!! "Token efficiency is going to be a big theme this year… because the spend has been ramping up way faster than enterprise customers thought." @DavidSacks said this on the latest @theallinpod Most AI tasks don’t need frontier-model reasoning. Small language models are bridging that gap. That’s what we’re building at @ZeroGPU_AI.
Read the original story cnbc.com/2026/06/03/ama…
So we stopped trying to build a data center, and started on a solution. An edge inference network built around idle compute. Run repeatable work on small and nano language models. Frontier models stay for reasoning. → zerogpu.ai
$700 billion is being spent on AI compute this year. Today a city voted to pause that spend. The buildout is hitting a wall — and most of what it’s being built for never needed a data center at all. 🧵
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